To address the limitations of existing research methods in terms of comprehensively measuring vehicle dimensions and accurately identifying vehicle information, this study proposes an innovative method for vehicle size and information recognition. First, the YOLO V5s network automatically recognizes target vehicles, followed by the creation of a vehicle database containing detailed attributes such as vehicle model, size, number of axles, wheelbase, suspension, and tire specifications. Second, vehicle contours are extracted using the convolutional encoder-decoder network and optimized using the dilation corrosion algorithm. To improve processing speed and reduce computational resource requirements, a depthwise separable convolution technique is integrated into the DenseNet network. Finally, the optimized DenseNet network predicts the vehicle's specific dimensions from the contour map. The experimental results show that the proposed method achieves width and height measurement errors within a range of ±60 mm, and length measurement errors within ±85 mm. In addition, this method provides information on number of axles, wheelbase, suspension, and tire parameters. This study demonstrates that by integrating image recognition and deep learning techniques, it is possible to effectively measure vehicle dimensions and identify relevant information, resulting in substantial improvements in accuracy for applications such as dynamic weighing systems.